Author

Date of Award

Document Type

Degree Name

Department

Electrical Engineering

First Advisor

Harlan J. Perlis

Second Advisor

John W. Liskowitz

Third Advisor

Sol Rosenstark

Fourth Advisor

W. H. Warren Ball

Abstract

A comprehensive theory of multiple measurements for the optimum on-line state estimation and parameter identification in a class of noisy, dynamic distributed systems, is developed in this study. Often in practical monitoring and control problems, accurate measurements of a critical variable are not available in a desired form or at a desired sampling rate. Rather, noisy independent measurements of related forms of the variable may be available at different sampling rates. Multiple measurements theory thus involves the optimum weighting and combination of different types of available measurements. One of the contributions of this work is the development of a unique measurement projection method by which off-line measurements may be optimally utilized for on-line estimation and control.

The analysis of distributed systems often requires the establishment of monitoring stations. Another contribution of this study is the development of a measurement strategy, based on statistical experimental design techniques, for the optimum spatial monitoring stations in a class of distributed systems.

By incorporating in the optimization criterion, terms representing the realistic costs of making observations, an algorithm is developed for an estimator indicator whose values dictate an observation strategy for the optimum number and temporal intervals of observations. This, along with the optimum measurement stations thus provides a comprehensive monitoring policy on which the estimation and control of a distributed system may be based.

By employing the measurement projection scheme and the monitoring policy, algorithms are further developed for Kalmantype distributed filters for the estimation of the state profiles based on all available on-line and off-line measurements.

In the interest of a realistic engineering application, the developments in this study are based on a specific class of distributed systems representable by the mass transport models in environmental pollution systems. However, the techniques developed are equally applicable to a broader class of systems, including process control, where measurements may be characterized by noisy on-line instrumentation and off-line empirical laboratory tests.

Although pertinent field data were not available for the research, the multiple measurements techniques developed were applied to several simulated numerical examples that do represent typical engineering problems. The results obtained demonstrate the consistent superiority of the techniques over existing estimation methods. Methods by which the results of this work may be integrated into real engineering problems are also discussed.